Tearing instability of a flat current layer with finite ionic viscosity

1985 ◽  
Vol 26 (2) ◽  
pp. 155-162
Author(s):  
V. V. Gatilov ◽  
A. M. Sagalakov ◽  
V. F. Ul'chenko
Keyword(s):  
Current Layer ◽  
Tearing Instability ◽  
Flat Current

scholarly journals Tubular Pinch and Tearing Instability

1969 ◽  
Vol 12 (10) ◽  
pp. 2099 ◽  
Author(s):  
Oscar A. Anderson
Keyword(s):  
Tearing Instability

scholarly journals Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3430
Author(s):  
Jean Mário Moreira de Lima ◽  
Fábio Meneghetti Ugulino de Araújo
Keyword(s):  
Short Term Memory ◽  
Soft Sensor ◽  
Fine Tuning ◽  
Current Layer ◽  
Layer By Layer ◽  
Linear Processes ◽  
Method Performance ◽  
Modeling Approach ◽  
Ensemble Strategy ◽  
Sensor Modeling

Soft sensors based on deep learning have been growing in industrial process applications, inferring hard-to-measure but crucial quality-related variables. However, applications may present strong non-linearity, dynamicity, and a lack of labeled data. To deal with the above-cited problems, the extraction of relevant features is becoming a field of interest in soft-sensing. A novel deep representative learning soft-sensor modeling approach is proposed based on stacked autoencoder (SAE), mutual information (MI), and long-short term memory (LSTM). SAE is trained layer by layer with MI evaluation performed between extracted features and targeted output to evaluate the relevance of learned representation in each layer. This approach highlights relevant information and eliminates irrelevant information from the current layer. Thus, deep output-related representative features are retrieved. In the supervised fine-tuning stage, an LSTM is coupled to the tail of the SAE to address system inherent dynamic behavior. Also, a k-fold cross-validation ensemble strategy is applied to enhance the soft-sensor reliability. Two real-world industrial non-linear processes are employed to evaluate the proposed method performance. The obtained results show improved prediction performance in comparison to other traditional and state-of-art methods. Compared to the other methods, the proposed model can generate more than 38.6% and 39.4% improvement of RMSE for the two analyzed industrial cases.

The thickness of the magnetopause current layer: ISEE 1 and 2 observations

1982 ◽  
Vol 87 (A4) ◽  
pp. 2108 ◽  
Author(s):  
Jean Berchem ◽  
C. T. Russell
Keyword(s):  
Current Layer

Kinetic theory of tearing instability

1975 ◽  
Vol 18 (12) ◽  
pp. 1778 ◽  
Author(s):  
R. D. Hazeltine ◽  
D. Dobrott ◽  
T. S. Wang
Keyword(s):  
Kinetic Theory ◽  
Tearing Instability

Double tearing instability with shear flow

1992 ◽  
Vol 4 (9) ◽  
pp. 2751-2757 ◽  
Author(s):  
L. Ofman
Keyword(s):  
Shear Flow ◽  
Tearing Instability

Two-fluid tearing instability in force-free magnetic configuration

2004 ◽  
Vol 11 (9) ◽  
pp. 4468-4482 ◽  
Author(s):  
V. V. Mirnov ◽  
C. C. Hegna ◽  
S. C. Prager
Keyword(s):  
Magnetic Configuration ◽  
Tearing Instability ◽  
Two Fluid

scholarly journals A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks

2020 ◽  
Author(s):  
B Wang ◽  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Learning Task ◽  
Fixed Number ◽  
Learning Rate ◽  
Current Layer ◽  
Training Process ◽  
Deep Convolutional Neural Networks

© 2019, Springer Nature Switzerland AG. Image classification is a difficult machine learning task, where Convolutional Neural Networks (CNNs) have been applied for over 20 years in order to solve the problem. In recent years, instead of the traditional way of only connecting the current layer with its next layer, shortcut connections have been proposed to connect the current layer with its forward layers apart from its next layer, which has been proved to be able to facilitate the training process of deep CNNs. However, there are various ways to build the shortcut connections, it is hard to manually design the best shortcut connections when solving a particular problem, especially given the design of the network architecture is already very challenging. In this paper, a hybrid evolutionary computation (EC) method is proposed to automatically evolve both the architecture of deep CNNs and the shortcut connections. Three major contributions of this work are: Firstly, a new encoding strategy is proposed to encode a CNN, where the architecture and the shortcut connections are encoded separately; Secondly, a hybrid two-level EC method, which combines particle swarm optimisation and genetic algorithms, is developed to search for the optimal CNNs; Lastly, an adjustable learning rate is introduced for the fitness evaluations, which provides a better learning rate for the training process given a fixed number of epochs. The proposed algorithm is evaluated on three widely used benchmark datasets of image classification and compared with 12 peer Non-EC based competitors and one EC based competitor. The experimental results demonstrate that the proposed method outperforms all of the peer competitors in terms of classification accuracy.

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